PROJECT SUMMARY/ABSTRACT Single-cell RNA-seq (scRNA-seq) analysis has been widely used to determine transcriptomic profiles at a single-cell resolution. A large number of computational methods have been developed to analyze scRNA-seq data. Most of these methods focus on better processing and interpretation of scRNA-seq data, and there is a lack of computational approaches for downstream analyses that generate novel biological hypotheses from scRNA-seq profiles. In this project, we propose to develop a new computational framework for scRNA-seq data to infer the regulatory activity of transcriptional factors (TFs) at the single-cell level, and then construct regulatory network associated with single-cell phenotypes. In contrast to all existing network inference methods, our framework determines regulatory interactions in single cells based on TF activities, rather than their expression levels. This is in line with the fact that TF functions are rarely reflected by their expression due to intensive post-transcriptional and post-translational events. To facilitate the application of this framework, we will construct a user-friendly database to release and update the software/packages, pipelines, and processed data profiles that will be produced from this project. Additionally, we will apply and test our framework in two in- house scRNA-seq datasets for melanoma and systemic sclerosis generated by our collaborators. This framework will provide useful tools to reveal regulatory mechanisms that determine the phenotypes of cells captured in scRNA-seq analyses. Considering the wide application of scRNA-seq approaches, we expect that our framework will benefit a broad range of research communities in biological and biomedical areas.